Adaptive PID tracking control based radial basic function networks for a 2-DOF parallel manipulator

Nguyen, Van‐Truong; Lin, Chia Ching; Su, Shun‐Feng; Tran, Quoc‐Viet

doi:10.1109/icsse.2017.8030887

Cited by 13 publications

(5 citation statements)

References 8 publications

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“…To train an RBFN, usually, only weights connecting the hidden layer and output layer are updated online in most of the previously proposed approaches [15][16][17][18][19][20][21][22][23][24][25][26][27][28]33,34]. In our study, based on the steepest-descent kind of learning algorithms and e-modification, the adaptive laws of RBFN with fully online update on the output weights and both the centers and the variances of the Gaussian functions are considered.…”

Section: Controller Designmentioning

confidence: 99%

“…A fuzzy integral SMC of continuous-time T-S fuzzy systems is developed with matched uncertainties and external disturbance in [18]. The hybrid scheme of using TSMC and RBFNs is analyzed in [19][20][21][22]. In a different direction, a hybrid controller that combines SMC and fuzzy neural network is discussed in [23][24][25][26][27][28] and [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

“…In a different direction, a hybrid controller that combines SMC and fuzzy neural network is discussed in [23][24][25][26][27][28] and [29][30][31][32]. The general idea in [15][16][17][18][19][20][21][22][23][24][25][26][27][28] is to create a smooth combination of various forms of SMC and computational intelligence techniques. Specifically, the learning algorithm based on computational intelligence techniques is used to handle complex uncertainties.…”

Section: Introductionmentioning

confidence: 99%

“…However, the stability of the nonlinear systems at a high‐speed motion is not considered in these controllers. Besides, in most of the previously proposed approaches [15–28,33,34], only weight values are updated online. Both the widths and the center values of the Gaussian function used in the approximation are given and fixed.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive finite‐time neural network control for redundant parallel manipulators

Nguyen

Wang

et al. 2019

Asian Journal of Control

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This paper reports our study of an adaptive finite-time neural network control scheme for redundant parallel manipulators. The proposed controller is based on fully tuned radial basis function neural networks (RBFNs), nonsingular fast terminal sliding mode control (NFTSMC), and nonlinearities in output feedback. RBFNs with a fully online update on the output weights and both the centers and variances of Gaussian functions are used to estimate system uncertainties and disturbances. The proposed approach possesses several advantages over other existing approaches like robustness, rapid response, nonsingularity, higher precision, finite-time convergence, and better tracking control performance. The stability of the parallel manipulators is ensured by the Lyapunov theory. Finally, simulation results validate the effectiveness of the proposed methodology.

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Section: Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive finite‐time neural network control for redundant parallel manipulators

Nguyen

Wang

et al. 2019

Asian Journal of Control

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“…On the other hand, combining other algorithm also can enhance the tracking performance of PID controller [21]- [23]. V. Nguyen et al [24] proposed radial basic function neural networks based PID (APID-RBFNs), which is used in tracking task of two degree of freedom parallel manipulator. P. C. Sau [25] compared fuzzy fractional-order PID (FFOPID) controller and fractionalorder PID (FOPID) for the control system of three-arm manipulator.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling and Torque Feedforward based Optimal Fuzzy PD control of a High-Speed Parallel Manipulator

Lin

Cui

2021

jrc

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Dynamic modeling and control of high-speed parallel manipulators are of importance due to their industrial applications deployed in production lines. However, there are still a number of open problems, such as the development of a precise dynamic model to be used in the model-based control design. This paper presents a four-limb parallel manipulator with Schönflies motion and its simplified dynamic modeling process. Then, in order to fix the issue that computed torque method control (CTC) will spend a lot of time to calculate dynamic parameters in real-time, offline torque feedforwardbased PD (TFPD) control law is adopted in the control system. At the same time, fuzzy logic is also used to tune the gains of PD controller to adapt to the variation of external disturbance and compensate the un-modeled uncertainty. Additionally, bottom widths of membership functions of fuzzy controller are optimized by bat algorithm. Finally, three controllers of CTC, TFPD and bat algorithm-based torque feedforwad fuzzy PD controller (BA-TFFPD) are compared in trajectory tracking simulation. Fro the result, compared with TFPD and CTC, BA-TFFPD can lead faster transient response and lower tracking error, which prove the validity of BA-TFFPD.

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Control the Movement of Mobile Robot Using Fingers Gestures Based on Fuzzy Logic

Nguyen

Bui

et al. 2022

Lecture Notes in Mechanical Engineering

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Adaptive PID tracking control based radial basic function networks for a 2-DOF parallel manipulator

Cited by 13 publications

References 8 publications

Adaptive finite‐time neural network control for redundant parallel manipulators

Adaptive finite‐time neural network control for redundant parallel manipulators

Dynamic Modeling and Torque Feedforward based Optimal Fuzzy PD control of a High-Speed Parallel Manipulator

Control the Movement of Mobile Robot Using Fingers Gestures Based on Fuzzy Logic

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